Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market

Aliasghari, Parinaz; Mohammadi‐Ivatloo, Behnam; Abapour, Mehdi; Ahmadian, Ali; Elkamel, Ali

doi:10.3390/en13071771

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…Nevertheless, its name first appeared in Charnes and Cooper (1961). Although GP was developed many years ago, it is still applied to numerous theoretical and practical problems (Spivey and Hirokuni 1970;Spronk 1981;Vrat and Kriengkrairut 1986;Giokas 1997;Lin et al 2009;Gaspars-Wieloch 2017a, 2018aGupta et al 2018;Gür and Eren 2018;Ismail et al 2019;Mensah and Rocca 2019;Aliasghari et al 2020;Khan et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A New Application for the Goal Programming—The Target Decision Rule for Uncertain Problems

Gaspars-Wieloch

2020

JRFM

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The goal programming (GP) is a well-known approach applied to multi-criteria decision making (M-DM). It has been used in many domains and the literature offers diverse extensions of this procedure. On the other hand, so far, some evident analogies between M-DM under certainty and scenario-based one-criterion decision making under uncertainty (1-DMU) have not been revealed in the literature. These similarities give the possibility to adjust the goal programming to an entirely new domain. The purpose of the paper is to create a novel method for uncertain problems on the basis of the GP ideas. In order to achieve this aim we carefully examine the analogies occurring between the structures of both issues (M-DM and 1-DMU). We also analyze some differences resulting from a different interpretation of the data. By analogy to the goal programming, four hybrids for 1-DMU are formulated. They differ from each other in terms of the type of the decision maker considered (pessimist, optimist, moderate). The new decision rule may be helpful when solving uncertain problems since it is especially designed for neutral criteria, which are not taken into account in existing procedures developed for 1-DMU.

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Section: Introductionmentioning

confidence: 99%

A New Application for the Goal Programming—The Target Decision Rule for Uncertain Problems

Gaspars-Wieloch

2020

JRFM

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“…Price is an effective method of charging demand management, thus attracting extensive research attention. Parinaaz [25] pointed out that the contract price level will affect the choice of electric vehicle aggregators for electric vehicle owners, and changing the contract price can realize the optimal scheduling of electric vehicle aggregators. Fang et al [26] defined fast-charging dynamic pricing as a Markov decision model, and experiments show that intelligent dynamic pricing can motivate the expansion of fast-charging infrastructure and maximize long-term profits.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Pricing iterative optimization for multi‐agent simulation of setting electric vehicle charging model in public parking lots

Mei

Liu

Zhao³

et al. 2022

IET Intelligent Trans Sys

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Despite the increasing scale of the electric vehicle market in recent years, in view of the long charging time of EVs (Electric Vehicles), the accessibility of charging facilities is still an obstacle to the rapid development of EVs. Therefore, the Chinese government has promulgated a one‐size‐fits‐all construction strategy which means constructing charging piles in parking lots with a fixed proportion. This paper mainly simulates the actual demand and adjusts the charging price of charging stations to reduce the uneven spatial distribution of charging demand. In particular, this paper constructs a multi‐agent system of the road network, vehicle, and charging station to simulate the charging behaviour in the mixed scenario of EVs and traditional fuel vehicles. The impact of charging pricing adjustment on individuals and charging stations is fed back in real‐time by using the characteristics of agents to respond to dynamic changes and make intelligent decisions. This paper proposes an iterative optimization method to obtain the optimal pricing strategy for charging stations to reduce the imbalance of charging demand. Taking the Wulin Square business district in Hangzhou as an example, the results show that the proposed pricing optimization method can improve the overall utility.

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“…In this way, optimal charging is achieved for all EVs, focusing on EVs' standpoint. A methodology is proposed in [13] to assess the value of the contract price and the charging and discharging processes regarding the EV aggregator. It also seeks the fair retail price for the EV owner in the day-ahead market.…”

Section: Related Workmentioning

confidence: 99%

Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

et al. 2020

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The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable for frequency regulation, which is essential for the proliferation of intermittent renewable sources. To this end, EV aggregators will arise as a market representative party on behalf of EVs. Thus, this player will be responsible for supplying the power needed to charge EVs, as well as offering their flexibility to support the system. The main goal of EV aggregators is to manage the potential participation of EVs in the reserve market, accounting for their charging and travel needs. This work follows this trend by conceiving a chance-constrained model able to optimize EVs participation in the reserve market, taking into account the uncertain behavior of EVs and their charging needs. The proposed model, includes penalties in the event of a failure in the provision of upward or downward reserve. Therefore, stochastic and chance-constrained programming are used to handle the uncertainty of a small fleet of EVs and the risk profile of the EV aggregator. Two different relaxation approaches, i.e., Big-M and McCormick, of the chance-constrained model are tested and validated for different number of scenarios and risk levels, based on an actual test case in Denmark with actual driving patterns. As a final remark, the McCormick relaxation presents better performance when the uncertainty budget increases, which is appropriated for large-scale problems.

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Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market

Cited by 12 publications

References 36 publications

A New Application for the Goal Programming—The Target Decision Rule for Uncertain Problems

A New Application for the Goal Programming—The Target Decision Rule for Uncertain Problems

Pricing iterative optimization for multi‐agent simulation of setting electric vehicle charging model in public parking lots

Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

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